Source medical images, such as from Computerized Axial Tomography (“CT” or “CAT”) or Magnetic Resonance Imaging (“MRI”) scanners, comprise images in which interior anatomical structures can be identified. See, for example, FIG. 1, which shows a typical scan image of interior anatomical structures. In general, CT scanners work by passing X-rays systematically through the body, while MRI scanners rely on a radio-sensitive effect caused by aligning water molecules within the body.
In the case of vascular structures, the anatomical structure being visualized can include bloodflow lumen, thrombus, calcified plaque, and non-calcified plaque. Bloodflow refers to that part of the vessel anatomy in which blood is flowing freely. Thrombus is clotted blood that is very thick and viscous. Calcified plaque is a hard, bone-like substance that forms within blood vessels and is a significant contributor to vessel stenosis.
The aorta is the main artery that takes blood from the heart, through the abdomen and into the lower part of the body. An aneurysm refers to a disease state in which the blood vessel wall becomes weakened and then “balloons” out in a characteristic way. An abdominal aortic aneurysm (“AAA”) refers to an abnormal, localized enlargement of the aorta in the region below the renal arteries (which feed the kidneys) and above the iliac bifurcation. See, for example, FIG. 2, which shows a typical abdominal aortic aneurysm. If left untreated, such an aneurysm will frequently continue to enlarge in size until it ultimately ruptures and causes death. The precise cause of AAA is unknown but is most commonly associated with atherosclerosis, hypertension and smoking.
The source medical images from CT or MRI scanners generally comprise a set of two-dimensional slices taken through the patient's body. Each slice comprises a two-dimensional matrix of intensity values (e.g., 0-4095) reflecting different tissue characteristics. These slices may be viewed in their native format (e.g., as an image created with different shades of darkness, according to the scan's intensity values). Alternatively, the intensity values within a particular slice may be analyzed and the boundaries and regions for each of the anatomical structures shown in that slice labeled or “segmented”. The segmented two-dimensional slices may the be viewed as individual slices or they may be further processed; using volume rendering techniques so as to create 3-dimensional computer models of the patient's anatomy, or 3-dimensional meshes of the isosurfaces representing the segmented boundaries may be constructed, or metrics such as volume or surface area may be calculated.
In connection with the foregoing, a problem associated with the prior art is that, in order to be quantitatively accurate and meaningful, the segmentation process must currently be conducted for every single slice of the source medical images.
Medical Metrx Solutions (formerly Medical Media Systems) of West Lebanon, New Hampshire (“MMS”) provides outsourced advanced imaging and three-dimensional reconstruction services. The processing services of MMS are a fundamentally different business model than conventional systems that offer workstation/software packages used for in-house three-dimensional modeling.
More particularly, conventional systems are generally based on either Maximum Intensity Projection (“MIPS”) or other automated segmentation and volume-rendering techniques. These conventional segmentation technologies, which are designed for general diagnostic, gross visualization of data, have the advantages of automatic segmentation processing, however, they also have the severe disadvantages of limited accuracy. The technical limitations of automatic segmentation processing include a substantial number of artifacts (i.e., missing or misleading anatomical elements), poor imaging of thrombus and small vessels, and the inability to accurately quantify anatomic measurements such as volume.
Instead of the automatic segmentation software used by conventional systems such as GE Advantage Windows™, Vital Images Vitrea™ and others, the MMS system is based on technician-guided segmentation in which axial slice data is manually reviewed and edited by highly trained technicians. MMS has compared its hand segmentation process to competing systems and found that for certain applications, including AAA modeling, the MMS system of hand segmenting image data produces models of superior accuracy. Among other things, the MMS hand segmentation process permits the creation of highly accurate polygon-based surface representations which provide the basis for the advanced MMS treatment planning software, Preview®, which includes multiple model objects. See, for example, FIG. 3, which shows a screen capture from the MMS Preview® system. The semantic nature of the MMS Preview® model enables extensive measurements to be made of the anatomy, including volumes, areas, distances, and computer-generated centerlines. The MMS image processing system is designed to ensure the highest standard of product quality and includes built-in metrics and methods for measuring that quality.
The MMS reconstruction software is designed to optimize the accuracy of segmentation of multiple anatomical structures when used by highly trained technicians. Proprietary segmentation tools allow for precise definition of bloodflow, thrombus/non-calcified plaque, calcium and other objects simultaneously during technician-guided processing of the CT or MRI scan data.
While conventional systems geared to produce automatic diagnostic output may take on the order of 15-30 minutes of operator time to run, at MMS it can take several hours to manually define the segmentation on every image of a 200 slice study. The fundamental limiting factor is the requirement for the technician to look at, and manipulate (e.g., to segment the different anatomical structures by “painting” on the native slice image), each slice image individually. At MMS, a typical study normally involves the processing of approximately 180 slices. However, with newer technology now becoming available, such as multi-detector CT machines, the number of source images can increase dramatically, e.g., by a factor of ten. This can dramatically increase the workload placed on the trained operator when conducting hand segmentation of the scan slices.
Thus, there is needed a way to reduce the processing time associated with hand segmenting every slice of a study without sacrificing model integrity.